This paper provides a comprehensive and, to our knowledge, the first review of inclusive human-computer interaction (HCI) within autonomous vehicles (AVs) and human-driven cars with partial autonomy, emphasizing accessibility and user-centered design principles. We explore the current technologies and HCI systems designed to enhance passenger experience, particularly for individuals with accessibility needs. Key technologies discussed include brain-computer interfaces, anthropomorphic interaction, virtual reality, augmented reality, mode adaptation, voice-activated interfaces, haptic feedback, etc. Each technology is evaluated for its role in creating an inclusive in-vehicle environment. Furthermore, we highlight recent interface designs by leading companies and review emerging concepts and prototypes under development or testing, which show significant potential to address diverse accessibility requirements. Safety considerations, ethical concerns, and adoption of AVs are other major issues that require thorough investigation. Building on these findings, we propose an end-to-end design framework that addresses accessibility requirements across diverse user demographics, including older adults and individuals with physical or cognitive impairments. This work provides actionable insights for designers, researchers, and policymakers aiming to create safer and more comfortable environments in autonomous and regular vehicles accessible to all users.

Face images provide significant biological information. Face age estimation is one of the primary research directions in face images. The appearance of the face changes dynamically, and these changes are influenced by a variety of factors such as light, aging, makeup, beard, etc. The human face has many characteristics, including emotions, sex, race, age, etc. Face age estimation has numerous applications in biometrics, security, commercial, military, interaction with computers, and providing services to the individual. In this paper, the age estimation system is divided into four distinct stages. The first step is to extract local features including Gabor wavelets (GW), Local Binary Pattern (LBP), Local Phase Quantization (LPQ), and Histogram of Oriented Gradients (HOG). These attributes are then combined in the second step as a feature fusion method, which combines four different feature extraction methods. In the following step, Particle Swarm Optimization (PSO) as a meta-heuristic optimization algorithm is used to decrease the size of attributes and find optimal features. Finally, we used classification and regression methods to estimate the age and age groups. Initially, we used support vector machines (SVMs) to determine the age class, and then used support vector regression (SVRs) to estimate the ages within those groups. Finally, our algorithm was examined on two widely used databases, i.e. the FG-NET and the MORPH, to determine its effectiveness regarding aging estimates. In the FGNET dataset, we achieved an MAE of 3.34 years and 75.69 percent classification accuracy, and in the MORPH dataset, we obtained an MAE of 3.21 years and 81.66 percent classification accuracy.

In recent decades, many successful meta-heuristic algorithms are widely applied to data clustering problems due to their powerful capabilities. The Chimp Optimization Algorithm (ChOA) is a recently proposed meta-heuristic search algorithm that is inspired by chimps' individual intelligence and sexual motivation in their group hunting. The good performance of ChOA in a variety of optimization problems, prove the superiority of this algorithm over other swarm-based intelligence. This study presents a novel approach according to ChOA for data clustering. ChOA, like other Swarm Intelligence-based Algorithms (SIAs), starts with a random population and then calculates an objective value for them. In data clustering problems the population of candidate solutions is the position vectors of the centroids, and the objective function is the sum of intra-cluster distances (SICDs) between each sample and its nearest centroid. Following that, ChOA attempts to optimize the population by finding the best position vectors of optimal centroids. There are four different kinds of chimps in this regard: driver, chaser, barrier, and attacker. Furthermore, four major hunting moves including driving, blocking, chasing, and attacking are considered to find the promising solution. In this research, four datasets of the UCI machine learning repository are used to evaluate the performance of the proposed new approach. Experimental results illustrate that the proposed work significantly outperforms other clustering algorithms regarding the objective value, Error Rate (ER), and some other statistical tests.